Read and programming decoding system for analog neural memory

ABSTRACT

Various examples of decoders and physical layout designs for non-volatile flash memory arrays in an analog neural system are disclosed. In one example, a system comprises a plurality of vector-by-matrix multiplication arrays in an analog neural memory system, each vector-by-matrix multiplication array comprising an array of non-volatile memory cells organized into rows and columns, wherein each memory cell comprises a word line terminal; a plurality of read row decoders, each read row decoder coupled to one of the plurality of vector-by-matrix multiplication arrays for applying a voltage to one or more selected rows during a read operation; and a shared program row decoder coupled to all of the plurality of vector-by-matrix multiplication arrays for applying a voltage to one or more selected rows in one or more of the vector-by-matrix multiplication arrays during a program operation.

PRIORITY CLAIM

This application is a divisional of U.S. patent application Ser. No.16/503,355, filed on Jul. 3, 2019, and titled, “Decoding System andPhysical Layout for Analog Neural Memory in Deep Learning ArtificialNeural Network,” which claims priority to U.S. Provisional PatentApplication No. 62/840,318, filed on Apr. 29, 2019, and titled,“Decoding System and Physical Layout for Analog Neural Memory in DeepLearning Artificial Neural Network,” which are incorporated by referenceherein.

FIELD OF THE INVENTION

Improved decoding systems are disclosed for analog neural memory systemsthat utilize non-volatile memory cells.

BACKGROUND OF THE INVENTION

Artificial neural networks mimic biological neural networks (the centralnervous systems of animals, in particular the brain) and are used toestimate or approximate functions that can depend on a large number ofinputs and are generally unknown. Artificial neural networks generallyinclude layers of interconnected “neurons” which exchange messagesbetween each other.

FIG. 1 illustrates an artificial neural network, where the circlesrepresent the inputs or layers of neurons. The connections (calledsynapses) are represented by arrows, and have numeric weights that canbe tuned based on experience. This makes neural networks adaptive toinputs and capable of learning. Typically, neural networks include alayer of multiple inputs. There are typically one or more intermediatelayers of neurons, and an output layer of neurons that provide theoutput of the neural network. The neurons at each level individually orcollectively make a decision based on the received data from thesynapses.

One of the major challenges in the development of artificial neuralnetworks for high-performance information processing is a lack ofadequate hardware technology. Indeed, practical neural networks rely ona very large number of synapses, enabling high connectivity betweenneurons, i.e. a very high computational parallelism. In principle, suchcomplexity can be achieved with digital supercomputers or specializedgraphics processing unit clusters. However, in addition to high cost,these approaches also suffer from mediocre energy efficiency as comparedto biological networks, which consume much less energy primarily becausethey perform low-precision analog computation. CMOS analog circuits havebeen used for artificial neural networks, but most CMOS-implementedsynapses have been too bulky given the high number of neurons andsynapses required.

Applicant previously disclosed an artificial (analog) neural networkthat utilizes one or more non-volatile memory arrays as the synapses inU.S. patent application Ser. No. 15/594,439, published as US PatentPublication No. 2017/0337466, which is incorporated by reference. Thenon-volatile memory arrays operate as an analog neural memory. Theneural network device includes a first plurality of synapses configuredto receive a first plurality of inputs and to generate therefrom a firstplurality of outputs, and a first plurality of neurons configured toreceive the first plurality of outputs. The first plurality of synapsesincludes a plurality of memory cells, wherein each of the memory cellsincludes spaced apart source and drain regions formed in a semiconductorsubstrate with a channel region extending there between, a floating gatedisposed over and insulated from a first portion of the channel regionand a non-floating gate disposed over and insulated from a secondportion of the channel region. Each of the plurality of memory cells isconfigured to store a weight value corresponding to a number ofelectrons on the floating gate. The plurality of memory cells isconfigured to multiply the first plurality of inputs by the storedweight values to generate the first plurality of outputs.

Each non-volatile memory cells used in the analog neural memory systemmust be erased and programmed to hold a very specific and precise amountof charge, i.e., the number of electrons, in the floating gate. Forexample, each floating gate must hold one of N different values, where Nis the number of different weights that can be indicated by each cell.Examples of N include 16, 32, 64, 128, and 256.

One challenge in vector by matrix multiplication (VMM) systems is theability to select a specific cell or groups of cells, or in some casesan entire array of cells, for erase, programming, and read operations. Arelated challenge is to improve, the use of physical space within asemiconductor die without losing functionality.

What is needed are improved decoding systems and physical layouts foranalog neural memory systems that utilize non-volatile memory cells.

SUMMARY OF THE INVENTION

Improved decoding systems and physical layouts are disclosed for analogneural memory systems that utilize non-volatile memory cells.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram that illustrates a prior art artificial neuralnetwork.

FIG. 2 depicts a prior art split gate flash memory cell

FIG. 3 depicts another prior art split gate flash memory cell

FIG. 4 depicts another prior art split gate flash memory cell.

FIG. 5 depicts another prior art split gate flash memory cell

FIG. 6 depicts another prior art split gate flash memory cell.

FIG. 7 depicts a prior art stacked gate flash memory cell.

FIG. 8 is a diagram illustrating the different levels of an exemplaryartificial neural network utilizing one or more non-volatile memoryarrays.

FIG. 9 is a block diagram illustrating a vector-by-matrix multiplicationsystem.

FIG. 10 is a block diagram illustrates an exemplary artificial neuralnetwork utilizing one or more a vector-by-matrix multiplication systems.

FIG. 11 depicts another embodiment of a vector-by-matrix multiplicationsystem.

FIG. 12 depicts another embodiment of a vector-by-matrix multiplicationsystem.

FIG. 13 depicts another embodiment of a vector-by-matrix multiplicationsystem.

FIG. 14 depicts another embodiment of a vector-by-matrix multiplicationsystem.

FIG. 15 depicts another embodiment of a vector-by-matrix multiplicationsystem.

FIG. 16 depicts a prior art long short term memory system.

FIG. 17 depicts an exemplary cell for use in a long short term memorysystem.

FIG. 18 depicts an embodiment of the exemplary cell of FIG. 17 .

FIG. 19 depicts another embodiment of the exemplary cell of FIG. 17 .

FIG. 20 depicts a prior art gated recurrent unit system.

FIG. 21 depicts an exemplary cell for use in a gated recurrent unitsystem.

FIG. 22 depicts an embodiment of the exemplary cell of FIG. 21 .

FIG. 23 depicts another embodiment of the exemplary cell of FIG. 21 .

FIG. 24 depicts another embodiment of a vector-by-matrix multiplicationsystem.

FIG. 25 depicts another embodiment of a vector-by-matrix multiplicationsystem.

FIG. 26 depicts another embodiment of a vector-by-matrix multiplicationsystem.

FIG. 27 depicts another embodiment of a vector-by-matrix multiplicationsystem.

FIG. 28 depicts another embodiment of a vector-by-matrix multiplicationsystem.

FIG. 29 depicts another embodiment of a vector-by-matrix multiplicationsystem.

FIG. 30 depicts another embodiment of a vector-by-matrix multiplicationsystem.

FIG. 31 depicts another embodiment of a vector-by-matrix multiplicationsystem.

FIG. 32 depicts another embodiment of a vector-by-matrix multiplicationsystem.

FIG. 33 depicts an exemplary block diagram of a vector-by-matrixmultiplication system.

FIG. 34 depicts an exemplary decoding embodiment of a vector-by-matrixmultiplication system.

FIG. 35 depicts another exemplary decoding embodiment of avector-by-matrix multiplication system.

FIG. 36 depicts an exemplary row decoder.

FIG. 37 depicts another exemplary decoding embodiment t of avector-by-matrix multiplication system.

FIG. 38 depicts another exemplary decoding embodiment of avector-by-matrix multiplication system.

FIG. 39 depicts another exemplary decoding embodiment of avector-by-matrix multiplication system.

FIG. 40 depicts an embodiment of a low voltage row decoder.

FIG. 41 depicts an embodiment of a combined low voltage row decoder andcontrol gate decoder.

FIG. 42 depicts an embodiment of a bit line decoder.

FIG. 43 depicts a vector-by-matrix multiplication system and an inputblock.

FIG. 44 depicts a multiplexor for receiving outputs from an array andproviding inputs in multiplexed fashion to one or more arrays.

FIGS. 45A and 45B depict exemplary layouts of a vector-by-matrixmultiplication system.

FIG. 46 depicts an exemplary layout of a vector-by-matrix multiplicationsystem.

FIG. 47 depicts a word line decoder circuit, a source line decodercircuit, and a high voltage level shifter for use with a vectormultiplier matrix.

FIG. 48 depicts an erase gate decoder circuit, a control gate decodercircuit, a source line decoder circuit, and a high voltage level shifterfor use with a vector multiplier matrix.

FIG. 49 depicts another embodiment of a word line driver for use with avector multiplier matrix.

FIG. 50 depicts another embodiment of a word line driver for use with avector multiplier matrix.

FIG. 51 depicts another exemplary decoding embodiment of avector-by-matrix multiplication system.

DETAILED DESCRIPTION OF THE INVENTION

The artificial neural networks of the present invention utilize acombination of CMOS technology and non-volatile memory arrays.

Non-Volatile Memory Cells

Digital non-volatile memories are well known. For example, U.S. Pat. No.5,029,130 (“the '130 patent”), which is incorporated herein byreference, discloses an array of split gate non-volatile memory cells,which are a type of flash memory cells. Such a memory cell 210 is shownin FIG. 2 . Each memory cell 210 includes source region 14 and drainregion 16 formed in semiconductor substrate 12, with channel region 18there between. Floating gate 20 is formed over and insulated from (andcontrols the conductivity of) a first portion of the channel region 18,and over a portion of the source region 14. Word line terminal 22 (whichis typically coupled to a word line) has a first portion that isdisposed over and insulated from (and controls the conductivity of) asecond portion of the channel region 18, and a second portion thatextends up and over the floating gate 20. The floating gate 20 and wordline terminal 22 are insulated from the substrate 12 by a gate oxide.Bitline terminal 24 is coupled to drain region 16.

Memory cell 210 is erased (where electrons are removed from the floatinggate) by placing a high positive voltage on the word line terminal 22,which causes electrons on the floating gate 20 to tunnel through theintermediate insulation from the floating gate 20 to the word lineterminal 22 via Fowler-Nordheim tunneling.

Memory cell 210 is programmed (where electrons are placed on thefloating gate) by placing a positive voltage on the word line terminal22, and a positive voltage on the source region 14. Electron currentwill flow from the source region 14 (source line terminal) towards thedrain region 16. The electrons will accelerate and become heated whenthey reach the gap between the word line terminal 22 and the floatinggate 20. Some of the heated electrons will be injected through the gateoxide onto the floating gate 20 due to the attractive electrostaticforce from the floating gate 20.

Memory cell 210 is read by placing positive read voltages on the drainregion 16 and word line terminal 22 (which turns on the portion of thechannel region 18 under the word line terminal). If the floating gate 20is positively charged (i.e. erased of electrons), then the portion ofthe channel region 18 under the floating gate 20 is turned on as well,and current will flow across the channel region 18, which is sensed asthe erased or “1” state. If the floating gate 20 is negatively charged(i.e. programmed with electrons), then the portion of the channel regionunder the floating gate 20 is mostly or entirely turned off, and currentwill not flow (or there will be little flow) across the channel region18, which is sensed as the programmed or “0” state.

Table No. 1 depicts typical voltage ranges that can be applied to theterminals of memory cell 110 for performing read, erase, and programoperations:

TABLE NO. 1 Operation of Flash Memory Cell 210 of FIG. 2 WL BL SL Read 10.5-3 V 0.1-2 V 0 V Read 2 0.5-3 V 0-2 V 2-0.1 V Erase ~11-13 V 0 V 0 VProgram 1-2 V 1-3 μA 9-10 V“Read 1” is a read mode in which the cell current is output on the bitline. “Read 2” is a read mode in which the cell current is output on thesource line terminal.

FIG. 3 shows memory cell 310, which is similar to memory cell 210 ofFIG. 2 with the addition of control gate (CG) terminal 28. Control gateterminal 28 is biased at a high voltage, e.g., 10V, in programming, lowor negative in erase, e.g., 0 v/−8V, low or mid range in read, e.g., 0v/2.5V. Other terminals are biased similarly to that of FIG. 2 .

FIG. 4 depicts four-gate memory cell 410 comprising source region 14,drain region 16, floating gate 20 over a first portion of channel region18, a select gate 22 (typically coupled to a word line, WL) over asecond portion of the channel region 18, a control gate 28 over thefloating gate 20, and an erase gate 30 over the source region 14. Thisconfiguration is described in U.S. Pat. No. 6,747,310, which isincorporated herein by reference for all purposes. Here, all gates arenon-floating gates except floating gate 20, meaning that they areelectrically connected or connectable to a voltage source. Programmingis performed by heated electrons from the channel region 18 injectingthemselves onto the floating gate 20. Erasing is performed by electronstunneling from the floating gate 20 to the erase gate 30.

Table No. 2 depicts typical voltage ranges that can be applied to theterminals of memory cell 310 for performing read, erase, and programoperations:

TABLE NO. 2 Operation of Flash Memory Cell 410 of FIG. 4 WL/SG BL CG EGSL Read 1 0.5-2 V 0.1-2 V 0-2.6 V 0-2.6 V 0 V Read 2 0.5-2 V 0-2 V 0-2.6V 0-2.6 V 2-0.1 V Erase −0.5 V/0 V 0 V 0 V/−8 V 8-12 V 0 V Program IVIgA 8-11 V 4.5-9 V 4.5-5 V“Read 1” is a read mode in which the cell current is output on the bitline. “Read 2” is a read mode in which the cell current is output on thesource line terminal.

FIG. 5 shows memory cell 510, which is similar to memory cell 410 ofFIG. 4 except that memory cell 510 does not contain an erase gate EGterminal. An erase is performed by biasing the substrate 18 to a highvoltage and biasing the control gate CG terminal 28 to a low or negativevoltage. Alternatively, an erase is performed by biasing word lineterminal 22 to a positive voltage and biasing control gate terminal 28to a negative voltage. Programming and reading is similar to that ofFIG. 4 .

FIG. 6 depicts a three-gate memory cell 610, which is another type offlash memory cell. Memory cell 610 is identical to the memory cell 410of FIG. 4 except that memory cell 610 does not have a separate controlgate terminal. The erase operation (whereby erasing occurs through useof the erase gate terminal) and read operation are similar to that ofthe FIG. 4 except there is no control gate bias applied. The programmingoperation also is done without the control gate bias, and as a result, ahigher voltage must be applied on the source line terminal during aprogram operation to compensate for a lack of control gate bias.

Table No. 3 depicts typical voltage ranges that can be applied to theterminals of memory cell 610 for performing read, erase, and programoperations:

TABLE NO. 3 Operation of Flash Memory Cell 610 of FIG. 6 WL/SG BL EG SLRead 1 0.5-2.2 V 0.1-2 V 0-2.6 V 0 V Read 2 0.5-2.2 V 0-2 V 0-2.6 V2-0.1 V Erase −0.5 V/0 V 0 V 11.5 V 0 V Program IV 2-3 μA 4.5 V 7-9 V“Read 1” is a read mode in which the cell current is output on the bitline. “Read 2” is a read mode in which the cell current is output on thesource line terminal.

FIG. 7 depicts stacked gate memory cell 710, which is another type offlash memory cell. Memory cell 710 is similar to memory cell 210 of FIG.2 , except that floating gate 20 extends over the entire channel region18, and control gate terminal 22 (which here will be coupled to a wordline) extends over floating gate 20, separated by an insulating layer(not shown). The erase, programming, and read operations operate in asimilar manner to that described previously for memory cell 210.

Table No. 4 depicts typical voltage ranges that can be applied to theterminals of memory cell 710 and substrate 12 for performing read,erase, and program operations:

TABLE NO. 4 Operation of Flash Memory Cell 710 of FIG. 7 CG BL SLSubstrate Read 1 0-5 V 0.1-2 V 0-2 V 0 V Read 2 0.5-2 V 0-2 V 2-0.1 V 0V Erase −8 to −10 V/0 V FLT FLT 8-10 V/15-20 V Program 8-12 V 3-5 V/0 V0 V/3-5 V 0 V

“Read 1” is a read mode in which the cell current is output on the bitline. “Read 2” is a read mode in which the cell current is output on thesource line terminal. Optionally, in arrays comprising rows and columnsof memory cells 210, 310, 410, 510, 610, or 710, source lines can becoupled to one row of memory cells or to two adjacent rows of memorycells. That is, source line terminals can be shared by adjacent rows ofmemory cells.

In order to utilize the memory arrays comprising one of the types ofnon-volatile memory cells described above in an artificial neuralnetwork, two modifications are made. First, the lines are configured sothat each memory cell can be individually programmed, erased, and readwithout adversely affecting the memory state of other memory cells inthe array, as further explained below. Second, continuous (analog)programming of the memory cells is provided.

Specifically, the memory state (i.e. charge on the floating gate) ofeach memory cell in the array can be continuously changed from a fullyerased state to a fully programmed state, independently and with minimaldisturbance of other memory cells. In another embodiment, the memorystate (i.e., charge on the floating gate) of each memory cell in thearray can be continuously changed from a fully programmed state to afully erased state, and vice-versa, independently and with minimaldisturbance of other memory cells. This means the cell storage is analogor at the very least can store one of many discrete values (such as 16or 64 different values), which allows for very precise and individualtuning of all the cells in the memory array, and which makes the memoryarray ideal for storing and making fine tuning adjustments to thesynapsis weights of the neural network.

The methods and means described herein may apply to other non-volatilememory technologies such as SONOS (silicon-oxide-nitride-oxide-silicon,charge trap in nitride), MONOS (metal-oxide-nitride-oxide-silicon, metalcharge trap in nitride), ReRAM (resistive ram), PCM (phase changememory), MRAM (magnetic ram), FeRAM (ferroelectric ram), OTP (bi-levelor multi-level one time programmable), and CeRAM (correlated electronram), without limitation. The methods and means described herein mayapply to volatile memory technologies used for neural network such asSRAM, DRAM, and volatile synapse cell, without limitation.

Neural Networks Employing Non-Volatile Memory Cell Arrays

FIG. 8 conceptually illustrates a non-limiting example of a neuralnetwork utilizing a non-volatile memory array of the presentembodiments. This example uses the non-volatile memory array neuralnetwork for a facial recognition application, but any other appropriateapplication could be implemented using a non-volatile memory array basedneural network.

S0 is the input layer, which for this example is a 32×32 pixel RGB imagewith 5 bit precision (i.e. three 32×32 pixel arrays, one for each colorR, G and B, each pixel being 5 bit precision). The synapses CB1 goingfrom input layer S0 to layer C1 apply different sets of weights in someinstances and shared weights in other instances, and scan the inputimage with 3×3 pixel overlapping filters (kernel), shifting the filterby 1 pixel (or more than 1 pixel as dictated by the model).Specifically, values for 9 pixels in a 3×3 portion of the image (i.e.,referred to as a filter or kernel) are provided to the synapses CB1,where these 9 input values are multiplied by the appropriate weightsand, after summing the outputs of that multiplication, a single outputvalue is determined and provided by a first synapse of CB1 forgenerating a pixel of one of the layers of feature map C1. The 3×3filter is then shifted one pixel to the right within input layer S0(i.e., adding the column of three pixels on the right, and dropping thecolumn of three pixels on the left), whereby the 9 pixel values in thisnewly positioned filter are provided to the synapses CB1, where they aremultiplied by the same weights and a second single output value isdetermined by the associated synapse. This process is continued untilthe 3×3 filter scans across the entire 32×32 pixel image of input layerS0, for all three colors and for all bits (precision values). Theprocess is then repeated using different sets of weights to generate adifferent feature map of C1, until all the features maps of layer C1have been calculated.

In layer C1, in the present example, there are 16 feature maps, with30×30 pixels each. Each pixel is a new feature pixel extracted frommultiplying the inputs and kernel, and therefore each feature map is atwo dimensional array, and thus in this example layer C1 constitutes 16layers of two dimensional arrays (keeping in mind that the layers andarrays referenced herein are logical relationships, not necessarilyphysical relationships—i.e., the arrays are not necessarily oriented inphysical two dimensional arrays). Each of the 16 feature maps in layerC1 is generated by one of sixteen different sets of synapse weightsapplied to the filter scans. The C1 feature maps could all be directedto different aspects of the same image feature, such as boundaryidentification. For example, the first map (generated using a firstweight set, shared for all scans used to generate this first map) couldidentify circular edges, the second map (generated using a second weightset different from the first weight set) could identify rectangularedges, or the aspect ratio of certain features, and so on.

An activation function P1 (pooling) is applied before going from layerC1 to layer S1, which pools values from consecutive, non-overlapping 2×2regions in each feature map. The purpose of the pooling function is toaverage out the nearby location (or a max function can also be used), toreduce the dependence of the edge location for example and to reduce thedata size before going to the next stage. At layer 51, there are 1615×15 feature maps (i.e., sixteen different arrays of 15×15 pixelseach). The synapses CB2 going from layer S1 to layer C2 scan maps in S1with 4×4 filters, with a filter shift of 1 pixel. At layer C2, there are22 12×12 feature maps. An activation function P2 (pooling) is appliedbefore going from layer C2 to layer S2, which pools values fromconsecutive non-overlapping 2×2 regions in each feature map. At layerS2, there are 22 6×6 feature maps. An activation function (pooling) isapplied at the synapses CB3 going from layer S2 to layer C3, where everyneuron in layer C3 connects to every map in layer S2 via a respectivesynapse of CB3. At layer C3, there are 64 neurons. The synapses CB4going from layer C3 to the output layer S3 fully connects C3 to S3, i.e.every neuron in layer C3 is connected to every neuron in layer S3. Theoutput at S3 includes 10 neurons, where the highest output neurondetermines the class. This output could, for example, be indicative ofan identification or classification of the contents of the originalimage.

Each layer of synapses is implemented using an array, or a portion of anarray, of non-volatile memory cells.

FIG. 9 is a block diagram of a system that can be used for that purpose.Vector-by-matrix multiplication (VMM) system 32 includes non-volatilememory cells and is utilized as the synapses (such as CB1, CB2, CB3, andCB4 in FIG. 6 ) between one layer and the next layer. Specifically, VMMsystem 32 includes VMM array 33 comprising non-volatile memory cellsarranged in rows and columns, erase gate and word line gate decoder 34,control gate decoder 35, bit line decoder 36 and source line decoder 37,which decode the respective inputs for the non-volatile memory cellarray 33. Input to VMM array 33 can be from the erase gate and wordlinegate decoder 34 or from the control gate decoder 35. Source line decoder37 in this example also decodes the output of VMM array 33.Alternatively, bit line decoder 36 can decode the output of VMM array33.

VMM array 33 serves two purposes. First, it stores the weights that willbe used by the VMM system 32. Second, VMM array 33 effectivelymultiplies the inputs by the weights stored in VMM array 33 and addsthem up per output line (source line or bit line) to produce the output,which will be the input to the next layer or input to the final layer.By performing the multiplication and addition function, VMM array 33negates the need for separate multiplication and addition logic circuitsand is also power efficient due to its in-situ memory computation.

The output of VMM array 33 is supplied to a differential summer (such asa summing op-amp or a summing current mirror) 38, which sums up theoutputs of VMM array 33 to create a single value for that convolution.The differential summer 38 is arranged to perform summation of bothpositive weight and negative weight inputs to output the single value.

The summed up output values of differential summer 38 are then suppliedto an activation function circuit 39, which rectifies the output. Theactivation function circuit 39 may provide sigmoid, tan h, ReLUfunctions, or any other non-linear function. The rectified output valuesof activation function circuit 39 become an element of a feature map ofthe next layer (e.g. C1 in FIG. 8 ), and are then applied to the nextsynapse to produce the next feature map layer or final layer. Therefore,in this example, VMM array 33 constitutes a plurality of synapses (whichreceive their inputs from the prior layer of neurons or from an inputlayer such as an image database), and summer 38 and activation functioncircuit 39 constitute a plurality of neurons.

The input to VMM system 32 in FIG. 9 (WLx, EGx, CGx, and optionally BLxand SLx) can be analog level, binary level, digital pulses (in whichcase a pulses-to-analog converter PAC may be needed to convert pulses tothe appropriate input analog level) or digital bits (in which case a DACis provided to convert digital bits to appropriate input analog level)and the output can be analog level, binary level, digital pulses, ordigital bits (in which case an output ADC is provided to convert outputanalog level into digital bits).

FIG. 10 is a block diagram depicting the usage of numerous layers of VMMsystems 32, here labeled as VMM systems 32 a, 32 b, 32 c, 32 d, and 32e. As shown in FIG. 10 , the input, denoted Inputx, is converted fromdigital to analog by a digital-to-analog converter 31, and provided toinput VMM system 32 a. The converted analog inputs could be voltage orcurrent. The input D/A conversion for the first layer could be done byusing a function or a LUT (look up table) that maps the inputs Inputx toappropriate analog levels for the matrix multiplier of input VMM system32 a. The input conversion could also be done by an analog to analog(A/A) converter to convert an external analog input to a mapped analoginput to the input VMM system 32 a. The input conversion could also bedone by a digital-to-digital pules (D/P) converter to convert anexternal digital input to a mapped digital pulse or pulses to the inputVMM system 32 a.

The output generated by input VMM system 32 a is provided as an input tothe next VMM system (hidden level 1) 32 b, which in turn generates anoutput that is provided as an input to the next VMM system (hidden level2) 32 c, and so on. The various layers of VMM system 32 function asdifferent layers of synapses and neurons of a convolutional neuralnetwork (CNN). Each VMM system 32 a, 32 b, 32 c, 32 d, and 32 e can be astand-alone, physical non-volatile memory array, or multiple VMM systemscould utilize different portions of the same physical non-volatilememory array, or multiple VMM systems could utilize overlapping portionsof the same physical non-volatile memory system. Each VMM system 32 a,32 b, 32 c, 32 d, and 32 e can also be time multiplexed for variousportion of its array or neurons. The example shown in FIG. 10 containsfive layers (32 a,32 b,32 c,32 d,32 e): one input layer (32 a), twohidden layers (32 b,32 c), and two fully connected layers (32 d,32 e).One of ordinary skill in the art will appreciate that this is merelyexemplary and that a system instead could comprise more than two hiddenlayers and more than two fully connected layers.

VMM Arrays

FIG. 11 depicts neuron VMM array 1100, which is particularly suited formemory cells 310 as shown in FIG. 3 , and is utilized as the synapsesand parts of neurons between an input layer and the next layer. VMMarray 1100 comprises memory array 1101 of non-volatile memory cells andreference array 1102 (at the top of the array) of non-volatile referencememory cells. Alternatively, another reference array can be placed atthe bottom.

In VMM array 1100, control gate lines, such as control gate line 1103,run in a vertical direction (hence reference array 1102 in the rowdirection is orthogonal to control gate line 1103), and erase gatelines, such as erase gate line 1104, run in a horizontal direction.Here, the inputs to VMM array 1100 are provided on the control gatelines (CG0, CG1, CG2, CG3), and the output of VMM array 1100 emerges onthe source lines (SL0, SL1). In one embodiment, only even rows are used,and in another embodiment, only odd rows are used. The current placed oneach source line (SL0, SL1, respectively) performs a summing function ofall the currents from the memory cells connected to that particularsource line.

As described herein for neural networks, the non-volatile memory cellsof VMM array 1100, i.e. the flash memory of VMM array 1100, arepreferably configured to operate in a sub-threshold region.

The non-volatile reference memory cells and the non-volatile memorycells described herein are biased in weak inversion:

Ids=Io*e ^((Vg−Vth)/nVt) =w*Io*e ^((Vg)/nVt),

-   -   where w=e^((−Vth)/nVt)        where Ids is the drain to source current; Vg is gate voltage on        the memory cell; Vth is threshold voltage of the memory cell; Vt        is thermal voltage=k*T/q with k being the Boltzmann constant, T        the temperature in Kelvin, and q the electronic charge; n is a        slope factor=1+(Cdep/Cox) with Cdep=capacitance of the depletion        layer, and Cox capacitance of the gate oxide layer; Io is the        memory cell current at gate voltage equal to threshold voltage,        Io is proportional to (Wt/L)*u*Cox*(n−1)*Vt² where u is carrier        mobility and Wt and L are width and length, respectively, of the        memory cell.

For an I-to-V log converter using a memory cell (such as a referencememory cell or a peripheral memory cell) or a transistor to convertinput current Ids, into an input voltage, Vg:

Vg=n*Vt*log [Ids/wp*Io]

Here, wp is w of a reference or peripheral memory cell.

For a memory array used as a vector matrix multiplier VMM array, theoutput current is:

Iout=wa*Io*e ^((Vg)/nVt), namely

Iout=(wa/wp)*Iin=W*Iin

W=e ^((Vthp−Vtha)/nVt)

Iin=wp*Io*e ^((Vg)/nVt)

Here, wa=w of each memory cell in the memory array.

A wordline or control gate can be used as the input for the memory cellfor the input voltage.

Alternatively, the non-volatile memory cells of VMM arrays describedherein can be configured to operate in the linear region:

Ids=beta*(Vgs−Vth)*Vds; beta=u*Cox*Wt/L,

Wα(Vgs−Vth),

meaning weight W in linear region is proportional to (Vgs−Vth)

A wordline or control gate or bitline or sourceline can be used as theinput for the memory cell operated in the linear region. The bitline orsourceline can be used as the output for the memory cell.

For an I-to-V linear converter, a memory cell (such as a referencememory cell or a peripheral memory cell) or a transistor operating inthe linear region or a resistor can be used to linearly convert aninput/output current into an input/output voltage.

Alternatively, the flash memory cells of VMM arrays described herein canbe configured to operate in the saturation region:

Ids=½*beta*(Vgs−Vth)²; beta=u*Cox*Wt/L

Wα(Vgs−Vth)², meaning weight W is proportional to (Vgs−Vth)²

A wordline, control gate, or erase gate can be used as the input for thememory cell operated in the saturation region. The bitline or sourcelinecan be used as the output for the output neuron.

Alternatively, the memory cells of VMM arrays described herein can beused in all regions or a combination thereof (sub threshold, linear, orsaturation).

Other embodiments for VMM array 32 of FIG. 9 are described in U.S.patent application Ser. No. 15/826,345, which is incorporated byreference herein. As described in that application, a sourceline or abitline can be used as the neuron output (current summation output).

FIG. 12 depicts neuron VMM array 1200, which is particularly suited formemory cells 210 as shown in FIG. 2 , and is utilized as the synapsesbetween an input layer and the next layer. VMM array 1200 comprises amemory array 1203 of non-volatile memory cells, reference array 1201 offirst non-volatile reference memory cells, and reference array 1202 ofsecond non-volatile reference memory cells. Reference arrays 1201 and1202, arranged in the column direction of the array, serve to convertcurrent inputs flowing into terminals BLR0, BLR1, BLR2, and BLR3 intovoltage inputs WL0, WL1, WL2, and WL3. In effect, the first and secondnon-volatile reference memory cells are diode-connected throughmultiplexors 1214 (only partially depicted) with current inputs flowinginto them. The reference cells are tuned (e.g., programmed) to targetreference levels. The target reference levels are provided by areference mini-array matrix (not shown).

Memory array 1203 serves two purposes. First, it stores the weights thatwill be used by the VMM array 1200 on respective memory cells thereof.Second, memory array 1203 effectively multiplies the inputs (i.e.current inputs provided in terminals BLR0, BLR1, BLR2, and BLR3, whichreference arrays 1201 and 1202 convert into the input voltages to supplyto wordlines WL0, WL1, WL2, and WL3) by the weights stored in the memoryarray 1203 and then adds all the results (memory cell currents) toproduce the output on the respective bit lines (BL0-BLN), which will bethe input to the next layer or input to the final layer. By performingthe multiplication and addition function, memory array 1203 negates theneed for separate multiplication and addition logic circuits and is alsopower efficient. Here, the voltage inputs are provided on the word linesWL0, WL1, WL2, and WL3, and the output emerges on the respective bitlines BL0-BLN during a read (inference) operation. The current placed oneach of the bit lines BL0-BLN performs a summing function of thecurrents from all non-volatile memory cells connected to that particularbitline.

Table No. 5 depicts operating voltages for VMM array 1200. The columnsin the table indicate the voltages placed on word lines for selectedcells, word lines for unselected cells, bit lines for selected cells,bit lines for unselected cells, source lines for selected cells, andsource lines for unselected cells, where FLT indicates floating, i.e. novoltage is imposed. The rows indicate the operations of read, erase, andprogram.

TABLE NO. 5 Operation of VMM Array 1200 of FIG. 12: WL WL -unsel BL BL-unsel SL SL -unsel Read 0.5-3.5 V −0.5 V/0 V 0.1-2 V (Ineuron) 0.6 V-2V/FLT 0 V 0 V Erase ~5-13 V 0 V 0 V 0 V 0 V 0 V Program 1-2 V −0.5 V/0 V0.1-3 uA Vinh ~2.5 V 4-10 V 0-1 V/FLT

FIG. 13 depicts neuron VMM array 1300, which is particularly suited formemory cells 210 as shown in FIG. 2 , and is utilized as the synapsesand parts of neurons between an input layer and the next layer. VMMarray 1300 comprises a memory array 1303 of non-volatile memory cells,reference array 1301 of first non-volatile reference memory cells, andreference array 1302 of second non-volatile reference memory cells.Reference arrays 1301 and 1302 run in row direction of the VMM array1300. VMM array is similar to VMM 1000 except that in VMM array 1300,the word lines run in the vertical direction. Here, the inputs areprovided on the word lines (WLA0, WLB0, WLA1, WLB2, WLA2, WLB2, WLA3,WLB3), and the output emerges on the source line (SL0, SL1) during aread operation. The current placed on each source line performs asumming function of all the currents from the memory cells connected tothat particular source line.

Table No. 6 depicts operating voltages for VMM array 1300. The columnsin the table indicate the voltages placed on word lines for selectedcells, word lines for unselected cells, bit lines for selected cells,bit lines for unselected cells, source lines for selected cells, andsource lines for unselected cells. The rows indicate the operations ofread, erase, and program.

TABLE NO. 6 Operation of VMM Array 1300 of FIG. 13 WL WL -unsel BL BL-unsel SL SL -unsel Read 0.5-3.5 V −0.5 V/0 V 0.1-2 V 0.1 V-2 V/FLT~0.3-1 V 0 V (Ineuron) Erase ~5-13 V 0 V 0 V 0 V 0 V SL-inhibit (~4-8 V)Program 1-2 V −0.5 V/0 V 0.1-3 uA Vinh ~2.5 V 4-10 V 0-1 V/FLT

FIG. 14 depicts neuron VMM array 1400, which is particularly suited formemory cells 310 as shown in FIG. 3 , and is utilized as the synapsesand parts of neurons between an input layer and the next layer. VMMarray 1400 comprises a memory array 1403 of non-volatile memory cells,reference array 1401 of first non-volatile reference memory cells, andreference array 1402 of second non-volatile reference memory cells.Reference arrays 1401 and 1402 serve to convert current inputs flowinginto terminals BLR0, BLR1, BLR2, and BLR3 into voltage inputs CG0, CG1,CG2, and CG3. In effect, the first and second non-volatile referencememory cells are diode-connected through multiplexors 1412 (onlypartially shown) with current inputs flowing into them through BLR0,BLR1, BLR2, and BLR3. Multiplexors 1412 each include a respectivemultiplexor 1405 and a cascoding transistor 1404 to ensure a constantvoltage on the bitline (such as BLR0) of each of the first and secondnon-volatile reference memory cells during a read operation. Thereference cells are tuned to target reference levels.

Memory array 1403 serves two purposes. First, it stores the weights thatwill be used by the VMM array 1400. Second, memory array 1403effectively multiplies the inputs (current inputs provided to terminalsBLR0, BLR1, BLR2, and BLR3, for which reference arrays 1401 and 1402convert these current inputs into the input voltages to supply to thecontrol gates (CG0, CG1, CG2, and CG3) by the weights stored in thememory array and then add all the results (cell currents) to produce theoutput, which appears on BL0-BLN, and will be the input to the nextlayer or input to the final layer. By performing the multiplication andaddition function, the memory array negates the need for separatemultiplication and addition logic circuits and is also power efficient.Here, the inputs are provided on the control gate lines (CG0, CG1, CG2,and CG3), and the output emerges on the bitlines (BL0-BLN) during a readoperation. The current placed on each bitline performs a summingfunction of all the currents from the memory cells connected to thatparticular bitline.

VMM array 1400 implements uni-directional tuning for non-volatile memorycells in memory array 1403. That is, each non-volatile memory cell iserased and then partially programmed until the desired charge on thefloating gate is reached. This can be performed, for example, using theprecision programming techniques described below. If too much charge isplaced on the floating gate (such that the wrong value is stored in thecell), the cell must be erased and the sequence of partial programmingoperations must start over. As shown, two rows sharing the same erasegate (such as EG0 or EG1) need to be erased together (which is known asa page erase), and thereafter, each cell is partially programmed untilthe desired charge on the floating gate is reached.

Table No. 7 depicts operating voltages for VMM array 1400. The columnsin the table indicate the voltages placed on word lines for selectedcells, word lines for unselected cells, bit lines for selected cells,bit lines for unselected cells, control gates for selected cells,control gates for unselected cells in the same sector as the selectedcells, control gates for unselected cells in a different sector than theselected cells, erase gates for selected cells, erase gates forunselected cells, source lines for selected cells, and source lines forunselected cells. The rows indicate the operations of read, erase, andprogram.

TABLE NO. 7 Operation of VMM Array 1400 of FIG. 14 CG - unsel WL - BL -same CG - EG - SL - WL unsel BL unsel CG sector unsel EG unsel SL unselRead 0.5-2 V −0.5 V/0 V 0.1-2 V 0 V/FLT 0-2.6 V 0-2.6 V 0-2.6 V 0-2.6 V0-2.6 V 0 V 0 V (Ineuron) Erase 0 V 0 V 0 V 0 V 0 V 0-2.6 V 0-2.6 V 5-12 V 0-2.6 V 0 V 0 V Program 0.7-1 V −0.5 V/0 V 0.1-1 uA Vinh 4-11 V0-2.6 V 0-2.6 V 4.5-5 V 0-2.6 V 4.5-5 V 0-1 V (1-2 V)

FIG. 15 depicts neuron VMM array 1500, which is particularly suited formemory cells 310 as shown in FIG. 3 , and is utilized as the synapsesand parts of neurons between an input layer and the next layer. VMMarray 1500 comprises a memory array 1503 of non-volatile memory cells,reference array 1501 or first non-volatile reference memory cells, andreference array 1502 of second non-volatile reference memory cells. EGlines EGR0, EG0, EG1 and EGR1 are run vertically while CG lines CG0,CG1, CG2 and CG3 and SL lines WL0, WL1, WL2 and WL3 are runhorizontally. VMM array 1500 is similar to VMM array 1400, except thatVMM array 1500 implements bi-directional tuning, where each individualcell can be completely erased, partially programmed, and partiallyerased as needed to reach the desired amount of charge on the floatinggate due to the use of separate EG lines. As shown, reference arrays1501 and 1502 convert input current in the terminal BLR0, BLR1, BLR2,and BLR3 into control gate voltages CG0, CG1, CG2, and CG3 (through theaction of diode-connected reference cells through multiplexors 1514) tobe applied to the memory cells in the row direction. The current output(neuron) is in the bitlines BL0-BLN, where each bit line sums allcurrents from the non-volatile memory cells connected to that particularbitline.

Table No. 8 depicts operating voltages for VMM array 1500. The columnsin the table indicate the voltages placed on word lines for selectedcells, word lines for unselected cells, bit lines for selected cells,bit lines for unselected cells, control gates for selected cells,control gates for unselected cells in the same sector as the selectedcells, control gates for unselected cells in a different sector than theselected cells, erase gates for selected cells, erase gates forunselected cells, source lines for selected cells, and source lines forunselected cells. The rows indicate the operations of read, erase, andprogram.

TABLE NO. 8 Operation of VMM Array 1500 of FIG. 15 CG -unsel WL - BL -same CG - EG - SL - WL unsel BL unsel CG sector unsel EG unsel SL unselRead 1.0-2 V −0.5 V/0 V 0.6-2 V 0 V/FLT 0-2.6 V 0-2.6 V 0-2.6 V 0-2.6 V0-2.6 V 0 V 0 V/FLT (Ineuron) Erase 0 V 0 V 0 V 0 V 0 V   4-9 V 0-2.6 V 5-12 V 0-2.6 V 0 V 0 V Program 0.7-1 V −0.5 V/0 V 0.1-1 uA Vinh 4-11 V0-2.6 V 0-2.6 V 4.5-5 V 0-2.6 V 4.5-5 V 0-1 V (1-2 V)

FIG. 24 depicts neuron VMM array 2400, which is particularly suited formemory cells 210 as shown in FIG. 2 , and is utilized as the synapsesand parts of neurons between an input layer and the next layer. In VMMarray 2400, the inputs INPUT₀, . . . , INPUT_(N) are received on bitlines BL₀, . . . BL_(N), respectively, and the outputs OUTPUT₁, OUTPUT₂,OUTPUT₃, and OUTPUT₄ are generated on source lines SL₀, SL₁, SL₂, andSL₃, respectively.

FIG. 25 depicts neuron VMM array 2500, which is particularly suited formemory cells 210 as shown in FIG. 2 , and is utilized as the synapsesand parts of neurons between an input layer and the next layer. In thisexample, the inputs INPUT₀, INPUT₁, INPUT₂, and INPUT₃ are received onsource lines SL₀, SL₁, SL₂, and SL₃, respectively, and the outputsOUTPUT₀, . . . OUTPUT_(N) are generated on bit lines BL₀, . . . ,BL_(N).

FIG. 26 depicts neuron VMM array 2600, which is particularly suited formemory cells 210 as shown in FIG. 2 , and is utilized as the synapsesand parts of neurons between an input layer and the next layer. In thisexample, the inputs INPUT₀, . . . , INPUT_(M) are received on word linesWL₀, . . . , WL_(M), respectively, and the outputs OUTPUT₀, . . .OUTPUT_(N) are generated on bit lines BL₀, . . . , BL_(N).

FIG. 27 depicts neuron VMM array 2700, which is particularly suited formemory cells 310 as shown in FIG. 3 , and is utilized as the synapsesand parts of neurons between an input layer and the next layer. In thisexample, the inputs INPUT₀, . . . , INPUT_(M) are received on word linesWL₀, . . . , WL_(M), respectively, and the outputs OUTPUT₀, . . .OUTPUT_(N) are generated on bit lines BL₀, . . . , BL_(N).

FIG. 28 depicts neuron VMM array 2800, which is particularly suited formemory cells 410 as shown in FIG. 4 , and is utilized as the synapsesand parts of neurons between an input layer and the next layer. In thisexample, the inputs INPUT₀, . . . , INPUT_(n) are received on verticalcontrol gate lines CG₀, . . . , CG_(N), respectively, and the outputsOUTPUT₁ and OUTPUT₂ are generated on source lines SL₀ and SL₁.

FIG. 29 depicts neuron VMM array 2900, which is particularly suited formemory cells 410 as shown in FIG. 4 , and is utilized as the synapsesand parts of neurons between an input layer and the next layer. In thisexample, the inputs INPUT₀, . . . , INPUT_(N) are received on the gatesof bit line control gates 2901-1, 2901-2, . . . , 2901-(N−1), and2901-N, respectively, which are coupled to bit lines BL₀, . . . BL_(N),respectively. Exemplary outputs OUTPUT₁ and OUTPUT₂ are generated onsource lines SL₀ and SL₁.

FIG. 30 depicts neuron VMM array 3000, which is particularly suited formemory cells 310 as shown in FIG. 3 , memory cells 510 as shown in FIG.5 , and memory cells 710 as shown in FIG. 7 , and is utilized as thesynapses and parts of neurons between an input layer and the next layer.In this example, the inputs INPUT₀, . . . , INPUT_(M) are received onword lines WL₀, . . . , WL_(M), and the outputs OUTPUT₀, . . . ,OUTPUT_(N) are generated on bit lines BL₀, . . . , BL_(N), respectively.

FIG. 31 depicts neuron VMM array 3100, which is particularly suited formemory cells 310 as shown in FIG. 3 , memory cells 510 as shown in FIG.5 , and memory cells 710 as shown in FIG. 7 , and is utilized as thesynapses and parts of neurons between an input layer and the next layer.In this example, the inputs INPUT₀, . . . , INPUT_(M) are received oncontrol gate lines CG₀, . . . , CG_(M). Outputs OUTPUT₀, . . . ,OUTPUT_(N) are generated on vertical source lines SL₀, . . . , SL_(N),respectively, where each source line SL_(i) is coupled to the sourcelines of all memory cells in column i.

FIG. 32 depicts neuron VMM array 3200, which is particularly suited formemory cells 310 as shown in FIG. 3 , memory cells 510 as shown in FIG.5 , and memory cells 710 as shown in FIG. 7 , and is utilized as thesynapses and parts of neurons between an input layer and the next layer.In this example, the inputs INPUT₀, . . . , INPUT_(M) are received oncontrol gate lines CG₀, . . . , CG_(M). Outputs OUTPUT₀, . . . ,OUTPUT_(N) are generated on vertical bit lines BL₀, . . . , BL_(N),respectively, where each bit line BL_(i) is coupled to the bit lines ofall memory cells in column i.

Long Short-Term Memory

The prior art includes a concept known as long short-term memory (LSTM).LSTM units often are used in neural networks. LSTM allows a neuralnetwork to remember information over predetermined arbitrary timeintervals and to use that information in subsequent operations. Aconventional LSTM unit comprises a cell, an input gate, an output gate,and a forget gate. The three gates regulate the flow of information intoand out of the cell and the time interval that the information isremembered in the LSTM. VMMs are particularly useful in LSTM units.

FIG. 16 depicts an exemplary LSTM 1600. LSTM 1600 in this examplecomprises cells 1601, 1602, 1603, and 1604. Cell 1601 receives inputvector x₀ and generates output vector h₀ and cell state vector c₀. Cell1602 receives input vector x₁, the output vector (hidden state) h₀ fromcell 1601, and cell state c₀ from cell 1601 and generates output vectorh₁ and cell state vector c₁. Cell 1603 receives input vector x₂, theoutput, vector (hidden state) h₁ from cell 1602, and cell state c₁ fromcell 1602 and generates output vector h₂ and cell state vector c₂. Cell1604 receives input vector x₃, the output vector (hidden state) h₂ fromcell 1603, and cell state c₂ from cell 1603 and generates output vectorh₃. Additional cells can be used, and an LSTM with four cells is merelyan example.

FIG. 17 depicts an exemplary implementation of an LSTM cell 1700, whichcan be used for cells 1601, 1602, 1603, and 1604 in FIG. 16 . LSTM cell1700 receives input vector x(t), cell state vector c(t−1) from apreceding cell, and output vector h(t−1) from a preceding cell, andgenerates cell state vector c(t) and output vector h(t).

LSTM cell 1700 comprises sigmoid function devices 1701, 1702, and 1703,each of which applies a number between 0 and 1 to control how much ofeach component in the input vector is allowed through to the outputvector. LSTM cell 1700 also comprises tan h devices 1704 and 1705 toapply a hyperbolic tangent function to an input vector, multiplierdevices 1706, 1707, and 1708 to multiply two vectors together, andaddition device 1709 to add two vectors together. Output vector h(t) canbe provided to the next LSTM cell in the system, or it can be accessedfor other purposes.

FIG. 18 depicts an LSTM cell 1800, which is an example of animplementation of LSTM cell 1700. For the reader's convenience, the samenumbering from LSTM cell 1700 is used in LSTM cell 1800. Sigmoidfunction devices 1701, 1702, and 1703 and tan h device 1704 eachcomprise multiple VMM arrays 1801 and activation circuit blocks 1802.Thus, it can be seen that VMM arrays are particular useful in LSTM cellsused in certain neural network systems. The multiplier devices 1706,1707, and 1708 and the addition device 1709 are implemented in a digitalmanner or in an analog manner. The activation function blocks 1802 canbe implemented in a digital manner or in an analog manner.

An alternative to LSTM cell 1800 (and another example of animplementation of LSTM cell 1700) is shown in FIG. 19 . In FIG. 19 ,sigmoid function devices 1701, 1702, and 1703 and tan h device 1704share the same physical hardware (VMM arrays 1901 and activationfunction block 1902) in a time-multiplexed fashion. LSTM cell 1900 alsocomprises multiplier device 1903 to multiply two vectors together,addition device 1908 to add two vectors together, tan h device 1705(which comprises activation circuit block 1902), register 1907 to storethe value i(t) when i(t) is output from sigmoid function block 1902,register 1904 to store the value f(t)*c(t−1) when that value is outputfrom multiplier device 1903 through multiplexor 1910, register 1905 tostore the value i(t)*u(t) when that value is output from multiplierdevice 1903 through multiplexor 1910, and register 1906 to store thevalue o(t)*c˜(t) when that value is output from multiplier device 1903through multiplexor 1910, and multiplexor 1909.

Whereas LSTM cell 1800 contains multiple sets of VMM arrays 1801 andrespective activation function blocks 1802, LSTM cell 1900 contains onlyone set of VMM arrays 1901 and activation function block 1902, which areused to represent multiple layers in the embodiment of ISM cell 1900.LSTM cell 1900 will require less space than ISM 1800, as LSTM cell 1900will require ¼ as much space for VMMs and activation function blockscompared to LSTM cell 1800.

It can be further appreciated that LSTM units will typically comprisemultiple VMM arrays, each of which requires functionality provided bycertain circuit blocks outside of the VMM arrays, such as a summer andactivation circuit block and high voltage generation blocks. Providingseparate circuit blocks for each VMM array would require a significantamount of space within the semiconductor device and would be somewhatinefficient.

Gated Recurrent Units

An analog VMM implementation can be utilized for a GRU (gated recurrentunit) system. GRUs are a gating mechanism in recurrent neural networks.GRUs are similar to LSTMs, except that GRU cells generally contain fewercomponents than an LSTM cell.

FIG. 20 depicts an exemplary GRU 2000. GRU 2000 in this examplecomprises cells 2001, 2002, 2003, and 2004. Cell 2001 receives inputvector x₀ and generates output vector h₀. Cell 2002 receives inputvector x₁, the output vector h₀ from cell 2001 and generates outputvector h₁. Cell 2003 receives input vector x₂ and the output vector(hidden state) h₁ from cell 2002 and generates output vector h₂. Cell2004 receives input vector x₃ and the output vector (hidden state) h₂from cell 2003 and generates output vector h₃. Additional cells can beused, and an GRU with four cells is merely an example.

FIG. 21 depicts an exemplary implementation of a GRU cell 2100, whichcan be used for cells 2001, 2002, 2003, and 2004 of FIG. 20 . GRU cell2100 receives input vector x(t) and output vector h(t−1) from apreceding GRU cell and generates output vector h(t), GRU cell 2100comprises sigmoid function devices 2101 and 2102, each of which appliesa number between 0 and 1 to components from output vector h(t−1) andinput vector x(t). GRU cell 2100 also comprises a tan h device 2103 toapply a hyperbolic tangent function to an input vector, a plurality ofmultiplier devices 2104, 2105, and 2106 to multiply two vectorstogether, an addition device 2107 to add two vectors together, and acomplementary device 2108 to subtract an input from 1 to generate anoutput.

FIG. 22 depicts a GRU cell 2200, which is an example of animplementation of GRU cell 2100. For the reader's convenience, the samenumbering from GRU cell 2100 is used in GRU cell 2200. As can be seen inFIG. 22 , sigmoid function devices 2101 and 2102, and tan h device 2103each comprise multiple VMM arrays 2201 and activation function blocks2202. Thus, it can be seen that WM arrays are of particular use in GRUcells used in certain neural network systems. The multiplier devices2104, 2105, 2106, the addition device 2107, and the complementary device2108 are implemented in a digital manner or in an analog manner. Theactivation function blocks 2202 can be implemented in a digital manneror in an analog manner.

An alternative to GRU cell 2200 (and another example of animplementation of GRU cell 2300) is shown in FIG. 23 . In FIG. 23 , GRUcell 2300 utilizes VIM arrays 2301 and activation function block 2302,which when configured as a sigmoid function applies a number between 0and 1 to control how much of each component in the input vector isallowed through to the output vector. In FIG. 23 , sigmoid functiondevices 2101 and 2102 and tan h device 2103 share the same physicalhardware (VMM arrays 2301 and activation function block 2302) in atime-multiplexed fashion. GRU cell 2300 also comprises multiplier device2303 to multiply two vectors together, addition device 2305 to add twovectors together, complementary device 2309 to subtract an input from 1to generate an output, multiplexor 2304, register 2306 to hold the valueh(t−1)*r(t) when that value is output from multiplier device 2303through multiplexor 2304, register 2307 to hold the value h(t−1)*z(t)when that value is output from multiplier device 2303 throughmultiplexor 2304, and register 2308 to hold the value h{circumflex over( )}(t)*(1−z(t)) when that value is output from multiplier device 2303through multiplexor 2304.

Whereas GRU cell 2200 contains multiple sets of VMM arrays 2201 andactivation function blocks 2202, GRU cell 2300 contains only one set ofVMM arrays 2301 and activation function block 2302, which are used torepresent multiple layers in the embodiment of GRU cell 2300. GRU cell2300 will require less space than GRU cell 2200, as GRU cell 2300 willrequire ⅓ as much space for VMMs and activation function blocks comparedto GRU cell 2200.

It can be further appreciated that GRU systems will typically comprisemultiple VMM arrays, each of which requires functionality provided bycertain circuit blocks outside of the VMM arrays, such as a summer andactivation circuit block and high voltage generation blocks. Providingseparate circuit blocks for each VMM array would require a significantamount of space within the semiconductor device and would be somewhatinefficient.

The input to the VMM arrays can be an analog level, a binary level, ordigital bits (in this case a DAC is needed to convert digital bits toappropriate input analog level) and the output can be an analog level, abinary level, or digital bits (in this case an output ADC is needed toconvert output analog level into digital bits).

For each memory cell in a VMM array, each weight W can be implemented bya single memory cell or by a differential cell or by two blend memorycells (average of 2 cells). In the differential cell case, two memorycells are needed to implement a weight W as a differential weight(W=W+−W−). In the two blend memory cells, two memory cells are needed toimplement a weight W as an average of two cells.

Decoding Systems and Physical Layout Embodiments for VMM Arrays

FIGS. 33-51 disclose various decoding systems and physical layouts forVMM arrays that can be used with any of the memory cell types describedpreviously with respect to FIGS. 2-7 , or with other non-volatile memorycells.

FIG. 33 depicts VMM system 3300. VMM system 3300 comprises VMM array3301 (which can be based on any of the VMM array designs discussedpreviously, such as VMM array 1000, 1100, 1200, 1300, 1400, 1500, 2400,2500, 2600, 2700, 2800, 2900, 3000, 3100, and 3200 or other VMMdesigns), low voltage row decoder 3302, high voltage row decoder 3303,column decoder 3304, column driver 3305, control logic 3306, biascircuit 3307, neuron output circuit block 3308, input VMM circuit block3309, algorithm controller 3310, high voltage generator block 3311,analog circuit block 3315, and control logic 3316.

Input circuit block 3309 serves as interface from an external input tothe input terminals of the memory array 3301. Input circuit block 3309can comprise a DAC (Digital-to-Analog Converter), DPC (Digital-to-PulseConverter), APC (Analog-to-Pulse Converter), IVC (Current-to-VoltageConverter), AAC (Analog-to-Analog Converter such as voltage to voltagescaler), or FAC (Frequency-to-Analog Converter), without limitation.Neuron output block 3308 serves as an interface from the memory arrayoutput to an external interface (not shown). Neuron output block 3308can comprise an ADC (Analog-to-Digital Converter), APC (Analog-to-PulseConverter), DPC (Digital-to-Pulse Converter), IVC (Current-to-VoltageConverter), or IFC (Current-to-Frequency Converter), without limitation.Neuron output block 3308 may include activation functions, normalizationcircuitry, and/or re-scaling circuitry, without limitation.

Low voltage row decoder 3302 provides a bias voltage for read andprogram operations and provides a decoding signal for high voltage rowdecoder 3303. High voltage row decoder 3303 provides a high voltage biassignal for program and erase operations.

Algorithm controller 3310 provides a controlling function for bit linesduring program, verify, and erase operations.

High voltage generator block 3311 comprises charge pump 3312, chargepump regulator 3313, and high voltage generation circuitry 3314 thatprovides the multiple voltages needed for the various program, erase,program verify, and read operations.

FIG. 34 depicts VMM system 3400, which is particularly suited for usewith memory cells of the type depicted in FIG. 4 as memory cell 410. VMMsystem 3400 comprises VMM arrays 3401, 3402, 3402, and 3404 (each whichcan be based on any of the VMM array designs discussed previously, suchas VMM array 1000, 1100, 1200, 1300, 1400, 1500, 2400, 2500, 2600, 2700,2800, 2900, 3000 and 31000, or other VMM array designs); low voltage rowdecoders 3405, 3406, 3407, and 3408; shared high voltage row decoder3409; word lines or word input lines 3411, 3412, 3413, and 3414; bitlines 3421, 3422, 3423, and 3424; control gate lines 3432, source lines3434, and erase gate lines 3434. The shared high voltage row decoder3409 provides the control gate line 3432, source lines 3434, and erasegate lines 3434. In this arrangement, word lines 3411, 3412, 3413, and3414 and bit lines 3421, 3422, 3423, and 3424 are parallel to oneanother. In one embodiment the wordlines and bitlines are arranged inthe vertical direction. Control gate lines 3432, source line lines 3434,and erase gate lines 3436 are parallel to one another and are arrangedin the horizontal direction, and therefore are perpendicular to wordlines or word input lines 3411, 3412, 3413, and 3414 and bit lines 3421,3422, 3423, and 3424.

In VMM system 3400, VMM arrays 3401, 3402, 3403, and 3404 share controlgate lines 3432, source line lines 3434, erase gate lines 3436, and highvoltage row decoder 3409. However, each of the arrays has its own lowvoltage row decoder, such that low voltage row decoder 3405 is used withVMM array 3401; low voltage row decoder 3406 is used with VMM array3402; low voltage row decoder 3407 is used with VMM array 3403; and lowvoltage row decoder 3408 is used with VMM array 3404. Advantageous tothis arrangement is the fact that word lines 3411, 3412, 3413, and 3414are arranged in the vertical direction, such that word lines 3411 can berouted solely to VMM array 3401, word lines 3412 can be routed solely toVMM array 3402, word lines 3413 can be routed solely to VMM array 3403,and word lines 3414 can be routed solely to VMM array 3404. This wouldbe very inefficient using a conventional layout where word lines arearranged in the horizontal direction for multiple VMM arrays sharing thesame high voltage decoder and same high voltage decoding lines

FIG. 35 depicts VMM system 3500, which is particularly suited for usewith memory cells of the type depicted in FIG. 4 as memory cell 410. VMMsystem 3500 is similar to VMM system 3300 of FIG. 33 except that VMMsystem 3500 contains separate word lines and low voltage row decodersfor read operations and programming operations.

VMM system 3500 comprises VMM arrays 3501, 3502, 3503, and 3504 (eachwhich can be based on any of the VMM design discussed previously, suchas VMM array 1000, 1100, 1200, 1300, 1400, 1500, 2400, 2500, 2600, 2700,2800, 2900, 3000, 3100, and 3200 or other VMM array designs); lowvoltage read row decoders 3505, 3506, 3507, and 3508; shared low voltageprogram row decoder 3530; shared high voltage row decoder 3509; readword lines or word input lines 3511, 3512, 3513, and 3514; programpre-decoding row line 3515; bit lines 3521, 3522, 3523, and 3524;control gate lines 3532, source lines 3533, and erase gate lines 3535.The shared high voltage row decoder 3509 provides the control gate lines3532, source line 3533, and erase gate lines 3535. In this layout, readword lines or word input lines 3511, 3512, 3513, and 3514, programpre-decoding row line 3515, and bit lines 3521, 3522, 3523, and 3524 areparallel to one another and are arranged in the vertical direction.Control gate lines 3532, source lines 3533, and erase gate lines 3535are parallel to one another and are arranged in the horizontaldirection, and therefore are perpendicular to read word lines or wordinput lines 3511, 3512, 3513, and 3514, program pre-decoding row line3515, and bit lines 3521, 3522, 3523, and 3524. In this VMM system 3500,the low voltage program row decoder 3530 is shared across multiple VMMarrays.

In VMM system 3500, VMM arrays 3501, 3502, 3503, and 3504 share controlgate lines 3532, source lines 3533, erase gate lines 3535, and highvoltage row decoder 3509. However, each of the VMM arrays has its ownlow voltage read row decoder, such that low voltage read row decoder3505 is used with VMM array 3501; low voltage read row decoder 3506 isused with VMM array 3502; low voltage read row decoder 3507 is used withVMM array 3503; and low voltage read row decoder 3508 is used with VMMarray 3504.

Advantageous to this layout is the fact that read word lines or wordinput lines 3511, 3512, 3513, and 3514 are arranged in the verticaldirection, such that word lines 3511 can be routed solely to VMM array3501, word lines 3512 can be routed solely to VMM array 3502, word lines3513 can be routed solely to VMM array 3503, and word lines 3514 can berouted solely to VMM array 3504. This would be very inefficient using aconventional layout where word lines are arranged in the horizontaldirection for multiple arrays sharing the same high voltage decoder andsame high voltage decoding lines. Notably, program pre-decoding row line3515 can be connected to any of VMM arrays 3501, 3502, 3503, and 3504through low voltage program row decoder 3530 such that cells in one ormore of those VMM arrays can be programmed at a time.

FIG. 36 depicts additional detail regarding certain aspects of VMMsystem 3500, particularly, detail regarding the low voltage row decoders3505, 3506, 3507 and 3508, exemplified as low voltage row decoder 3600.Low voltage read row decoder 3600 comprises a plurality of switches,such as the exemplary switches shown, to selectively couple word lineswith rows of cells in VMM arrays 3601, 3602, 3603, and 3604,respectively. Low voltage program decoder 3630 comprises exemplary NANDgates 3631 and 3632, PMOS transistors 3633 and 3635 and NMOS transistors3636 and 3636, configured as shown. NAND gates 3631 and 3632 receiveprogram pre-decoding row lines XPs 3615 as inputs. During programoperation, switches Sp (which can be CMOS multiplexors or another typeof switch) in the low voltage read row decoders 3605, 3605, 3606, and3608 are closed, and thus the program wordline Wlp0-n are coupled to theword-lines in the array to apply voltages for programming. During a readoperation, read word lines or word input lines 3611, 3612, 3613, and3614 are selectively coupled to apply voltages to word line terminals ofrows within one or more of arrays 3601, 3602, 3603, and 3604 using theSr switches (being closed) (which can be CMOS multiplexors or anothertype of switch) within low voltage read row decoders 3605, 3606, 3607,and 3608.

FIG. 37 depicts VMM system 3700, which is particularly suited for usewith memory cells of the type depicted in FIG. 4 as memory cell 410. VMMsystem 3700 comprises VMM arrays 3701, 3702, 3702, and 3704 (each whichcan be based on any of the VMM design discussed previously, such as VMMarray 1000, 1100, 1200, 1300, 1400, 1500, 2400, 2500, 2600, 2700, 2800,2900, 3000 and 3100, or other VMM array designs); low voltage rowdecoders 3705, 3706, 3707, and 3708; local high voltage row decoders3709 and 3710; global high voltage row decoder 3730; word lines 3711,3712, 3713, and 3714; bit lines 3721, 3722, 3723, and 3724; high voltageand/or low voltage (HV/LV) pre-decoding lines 3732, source lines 3733,and erase gate lines 3734. The shared global high voltage row decoder3730 provides the HV/LV pre-decoding lines 3732, source line lines 3733,and erase gate lines 3734. In this layout, word-lines 3711, 3712, 3713,and 3714 and bit lines 3721, 3722, 3723, and 3724 are parallel to oneanother and are arranged in the vertical direction. HV/LV pre-decodinglines 3732, source line lines 3733, and erase gate lines 3734 areparallel to one another and are arranged in the horizontal direction,and therefore are perpendicular to word lines 3711, 3712, 3713, and 3714and bit lines 3721, 3722, 3723, and 3724. The HV/LV pre-decoding lines3732 are input to the local high voltage decoders 3709 and 3710. Thelocal high voltage decoders 3709 outputs the local control gate linesfor the VMM array 3701 and 3702. The local high voltage decoders 3710outputs the local control gate lines for the VMM array 3703 and 3704. Inanother embodiment, the local high voltage decoders 3709 and 3710 canprovide the local source lines for the VMM array 3701/3702 and VMM array3703/3704 respectively. In another embodiment, the local high voltagedecoders 3709 and 3710 can provide the local erase gate lines for theVMM array 3701/3702 and VMM array 3703/3704 respectively.

Here, local high voltage row decoder 3709 is shared by VMM arrays 3701and 3702 and local high voltage row decoder 3710 is shared by VMM arrays3703 and 3704. Global high voltage decoder 3730 routes high voltage andlow voltage pre-decoding signals to a local high voltage row decoder,such as local high voltage row decoders 3709 and 3710. Thus, the highvoltage decoding function is split between global high voltage rowdecoder 3730 and the local high voltage decoders such as local highvoltage decoders 3709 and 3710.

In VMM system 3700, VMM arrays 3701, 3702, 3703, and 3704 share HV/LVpre-decoding lines 3732, source lines 3733, erase gate lines 3734, andglobal high voltage row decoder 3730. However, each of the VMM arrayshas its own low voltage row decoder, such that low voltage row decoder3705 is used with VMM array 3701; low voltage row decoder 3706 is usedwith VMM array 3702; low voltage row decoder 3707 is used with VMM array3703; and low voltage row decoder 3708 is used with VMM array 3704.Advantageous to this layout is the fact that word lines 3711, 3712,3713, and 3714 are arranged in the vertical direction, such that wordlines 3711 can be routed solely to VMM array 3701, word lines 3712 canbe routed solely to VMM array 3702, word lines 3713 can be routed solelyto VMM array 3703, and word lines 3714 can be routed solely to VMM array3704. This would be very inefficient using a conventional layout whereword lines are arranged in the horizontal direction for multiple arrayssharing a single high voltage decoder.

FIG. 38 depicts VMM system 3800, which is particularly suited for usewith memory cells of the type depicted in FIG. 4 as memory cell 410. VMMsystem 3800 comprises VMM arrays 3801, 3802, 3802, and 3804 (each whichcan be based on any of the VMM design discussed previously, such as VMMarray 1000, 1100, 1200, 1300, 1400, 1500, 2400, 2500, 2600, 2700, 2800,2900, 3000, 3100, and 3200 or other VMM array designs); low voltage rowdecoders 3805, 3806, 3807, and 3808; local high voltage row decoders3809 and 3810; global high voltage row decoder 3830; bit lines 3821,3822, 3823, and 3824; control gate lines or control gate input lines3811 and 3812, HV/LV pre-decoding lines 3833, source lines 3834, anderase gate lines 3835. The shared global high voltage row decoder 3830provides the HV/LV pre-decoding line 3833, source line lines 3834, anderase gate lines 3835. The local high voltage decoders 3809 and 3810couples the control gate input CGs 3811 and 3812 to local control gatesof the VMM arrays 3801, 3802 and 3803, 3804 respectively. The lowvoltage row decoders 3805, 3806, 3807 and 3808 provide local(horizontal) word-lines to the arrays 3801, 3802, 3803, 3804respectively. In this layout, control gate lines 3811 and 3812 and bitlines 3821, 3822, 3823, and 3824 are parallel to one another and arearranged in the vertical direction. Source lines 3834 and erase gatelines 3835 are parallel to one another and are arranged in thehorizontal direction, and therefore are perpendicular to control gatelines 3811 and 3812 and bit lines 3821, 3822, 3823, and 3824.

As in VMM system 3700 of FIG. 37 , local high voltage row decoder 3809is shared by VMM arrays 3801 and 3802 and local high voltage row decoder3810 is shared by VMM arrays 3803 and 3804. Global high voltage decoder3830 routes signals to a local high voltage row decoder, such as localhigh voltage row decoders 3809 and 3810. Thus, the high voltage decodingfunction is split between global high voltage row decoder 3830 and thelocal high voltage decoders such as local high voltage decoders 3809 and3810 (that can provide local source line lines and/or local erase gatelines).

In VMM system 3800, VMM arrays 3801, 3802, 3803, and 3804 share HV/LVpre-decoding lines 3833, source line lines 3834, erase gate lines 3835,and global high voltage row decoder 3830. However, each of the VMMarrays has its own low voltage row decoder, such that low voltage rowdecoder 3805 is used with VMM array 3801; low voltage row decoder 3806is used with VMM array 3802; low voltage row decoder 3807 is used withVMM array 3803; and low voltage row decoder 3808 is used with VMM array3804. Advantageous to this layout is the fact that control gate lines3811 and 3812, which may be read lines or input lines, are arranged inthe vertical direction, such that control gate lines 3811 can be routedsolely to VMM arrays 3801 and 3802 and control gate lines 3812 can berouted solely to VMM arrays 3803 and 3804. This would not be possibleusing a conventional layout where word lines are arranged in thehorizontal direction.

FIG. 39 depicts VMM system 3900, which is particularly suited for usewith memory cells of the type depicted in FIG. 3 as memory cell 310,FIG. 4 as memory cell 410, FIG. 5 as memory cell 510, or FIG. 7 asmemory cell 710. VMM system 3900 comprises VMM arrays 3901 and 3902(each which can be based on any of the VMM design discussed previously,such as VMM array 1000, 1100, 1200, 1300, 1400, 1500, 2400, 2500, 2600,2700, 2800, 2900, 3000, 3100, and 3200 or other VMM array designs); lowvoltage row decoders 3903 (used with arrays 3901 and 3902); local highvoltage row decoder 3905, global high voltage row decoder 3904; controlgate lines 3908 and 3909; and bit lines 3906 and 3907. In this layout,control gate lines 3908 are used solely by VMM array 3901, and controlgate lines 3909 are used solely by VMM array 3902. Low voltage rowdecoding line 3910 is used as decoding input to the global high voltagerow decoder 3904. Global high voltage row decoding line 3911 is used asdecoding input to the local high voltage decoder 3905.

Local high voltage row decoder 3905 is shared by VMM arrays 3901 and3902. Global high voltage decoder 3904 routes signals to a local highvoltage row decoder of multiple VMM systems, such as local high voltagerow decoder 3905 of VMM system 3900. Thus, the high voltage decodingfunction is split between global high voltage row decoder 3904 and thelocal high voltage decoders such as local high voltage decoder 3905 asdescribed above.

In VMM system 3900, VMM arrays 3901 and 3902 share word lines (notshown), source gate lines if present (not shown), erase gate lines ifpresent (not shown), and global high voltage row decoder 3904. Here, VMMarrays 3901 and 3902 share low voltage row decoder 3903. Advantageous tothis layout is the fact that VMM arrays 3901 and 3902 do not sharecontrol gate lines, which enable each array to be independently accessedusing control gate lines 3908 and 3909, respectively.

FIG. 51 depicts VMM system 5100, which is particularly suited for usewith memory cells of the type depicted in FIG. 4 as memory cell 410. VMMsystem 5100 comprises VMM arrays 5101, 5102, 5103, and 5104 (each whichcan be based on any of the VMM array designs discussed previously, suchas VMM array 1000, 1100, 1200, 1300, 1400, 1510, 2400, 2510, 2600, 2700,2800, 2900, 3000, 3100, and 3200 or other VMM array designs); highvoltage decoder 5130; routing blocks 5151 and 5152; input word lines5111 and 5112, bit lines 5121, 5122, 5123, and 5124; control gate lines5132, source lines 5133, and erase gate lines 5134. The high voltagedecoder 5130 provides control gate lines 5132, source lines 5133, anderase gate lines 5134. The routing blocks 5151, 5152 is where the inputwordlines 5111 and 5112, respectively, which are received vertically,are routed to horizontal-running wordlines of VMM arrays 5101-5104.Alternatively, the routing blocks 5151, 5152 may route the control gateinput lines 5132 which are received vertically to horizontal-runningcontrol gate lines 5132 of the VMM arrays.

FIG. 40 depicts low voltage row decoder 4000, which comprises NAND gate4001, PMOS transistor 4002, and NMOS transistor 4003. NAND gate 4001receives row address signals 4004. PMOS transistor 4002 is coupled tovertical wordline inputs 4005. The output is on horizontal word lines4006, which is one of many word lines, which couple to respective VMMarrays. In this example, there are 16 word lines total, and there willtherefore be 16 instantiations of row decoder 4000, each outputting oneof the 16 word lines. Thus, based on the received row address signal,one word line, such as word line 4006, will output a respective signal,such as a voltage, and the other word lines will be set to ground.

FIG. 41 depicts combined co-select/deselect word line and control gatedecoder 4100, which comprises a low voltage row decoder as in FIG. 40 ,here comprising NAND gate 4101, PMOS transistor 4102, NMOS transistor4103, row address signals 4104, vertical input wordline lines 4105, andhorizontal word output line 4106 which couples to wordlines of VMMarrays. Combined word line and control gate decoder 4100 furthercomprises inverter 4107, switches 4108 and 4112, and isolationtransistor 4109, and receives control gate input 4110 CGIN0 and outputscontrol gate line 4111 CG0. The wordline output 4106 WL0 and controlgate output CG0 4111 are selected or de-selected at the same times bydecoding logic (not shown) controlling NAND gate 4101.

FIG. 42 depicts bit line decoder 4200, which operates on VMM arrays 4201and 4202. Bit line decoder 4200 comprises column multiplexor 4203 (forselecting one or more bit lines for program and verify, where a verifyoperation is used to confirm the cell current reaches a certain targetduring a tuning operation (program or erase operation), and senseamplifiers 4204 (for performing a read operation on one or more bitlines). As shown, local bitline mux 4201 b and 4202 b muxes local arraybitlines to global bitlines 4220 x to be coupled to the columnmultiplexor 4203. The sense amplifier comprises an ADC or other device.Thus, the bit line decoder 4200 is shared across multiple arrays.

FIG. 43 depicts VMM system 4300, which comprises VMM arrays 4301, 4302,4303, and 4304; low voltage row decoders 4305 and 4307; local highvoltage row decoders 4306 and 4308, global high voltage row decoder4309, digital bus inputs QIN[7:0] 4311 and 4312 (which here are inputsto a VMM array), and bit lines 4321, 4322, 4323, and 4324. Each lowvoltage row decoder, such as low voltage row decoder 4305, comprises acircuit block row decoder 4335 for each word line, such as exemplarydata input block 4331 (which might consist of 8 latches or registers)and block 4332 (which might comprise data-to-voltage converter circuitsor data-to-pulse converter circuits), which outputs signal 4333 on aword line. Thus, the input to this low voltage row decoder is a digitalbus QIN [7:0] with appropriate control logic. For each circuit block rowdecoder 4335, the digital input QIN [7:0] 4311 and 4312 are latchedappropriately such as by synchronous clocking means and method (such asby a serial to parallel clocking interface).

FIG. 44 depicts a neural network array input-output bus multiplexor4400, which receives outputs from a VMM array (such as from an ADC) andprovides those outputs in groups in multiplexed fashion to the inputblocks of other VMM arrays (such as DAC or DPC). In the example shown,the inputs to input-output bus multiplexor 4400 comprise 2048 bits (256sets, NEU0 . . . NEU255, of 8 bits each) and input-output busmultiplexor 4400 provides those bits in 64 different groups of 32 bitsper group, where it multiplexes between the different groups, such as byusing time-division multiplexing (where it provides 1 group of 32 bitsat any given time). Control logic 4401 generates control signals 4402 tocontrols input-output bus multiplexor 4400.

FIGS. 45A and 45B depict exemplary layouts of VMM arrays where the wordlines are laid out in a horizontal manner (FIG. 45A) versus in avertical manner (FIG. 45B, such as in FIG. 34 or 35 ).

FIG. 46 depicts an exemplary layout of VMM array where the word linesare laid out in a vertical manner (such as in FIG. 34 or 35 ). However,in this layout, two word lines (such as word lines 4601 and 4602) canoccupy the same column, but access different rows in the array (due tothe gap between them).

FIG. 47 depicts WM high voltage decode circuits, comprising word linedecoder circuit 4701, source line decoder circuit 4704, and high voltagelevel shifter 4708, which are appropriate for use with memory cells ofthe type shown in FIG. 2 .

Word line decoder circuit 4701 comprises PMOS select transistor 4702(controlled by signal HVO_B) and NMOS de-select transistor 4703(controlled by signal HVO_B) configured as shown.

Source line decoder circuit 4704 comprises NMOS monitor transistors 4705(controlled by signal HVO), driving transistor 4706 (controlled bysignal HVO), and de-select transistor 4707 (controlled by signal HVO_B),configured as shown.

High voltage level shifter 4708 receives enable signal EN and outputshigh voltage signal HV and its complement HVO_B.

FIG. 48 depicts VMM high voltage decode circuits, comprising erase gatedecoder circuit 4801, control gate decoder circuit 4804, source linedecoder circuit 4807, and high voltage level shifter 4811, which areappropriate for use with memory cells of the type shown in FIG. 3 .

Erase gate decoder circuit 4801 and control gate decoder circuit 4804use the same design as word line decoder circuit 4701 in FIG. 47 .

Source line decoder circuit 4807 uses the same design as source linedecoder circuit 4704 in FIG. 47 .

High voltage level shifter 4811 uses the same design as high voltagelevel shifter 4708 in FIG. 47 .

FIG. 49 depicts word line driver 4900. Word line driver 4900 selects aword line (such as exemplary word lines WL0, WL1, WL2, and WL3 shownhere) and provides a bias voltage to that word line. Each word line isattached to a select isolation transistor, such as select transistor4901, that is controlled by control line 4902. The select transistors,such as select transistor 4901, isolate the high voltage used during anerase operation (e.g., 8-12V) from word line decoding transistors, whichcan be implemented with TO transistors that operate at a low voltage(e.g., 1.8V, 3.3V). Here, during any operation, control line 4902 isactivated and all select transistors similar to select transistor 4901are turned on. Exemplary bias transistor 4903 (part of a wordlinedecoding circuit) selectively couples a word line to a first biasvoltage (such as 3V) and exemplary bias transistor 4904 (part of thewordline decoding circuit) selectively couples a word line to a secondbias voltage (lower than the first bias voltage, including ground, abias in between ground and the first bias voltage, or a negative voltagebias to reduce leakage from un-used memory rows). During an ANN (analogneural network) read operation, all used word lines will be selected andtied to the first bias voltage. All un-used wordlines are tied to thesecond bias voltage. During other operations such as program operation,only one word line will be selected and the other word lines will betied to the second bias voltage, which can be a negative bias (e.g.,−0.3 to −0.5V or more) to reduce array leakage.

Bias transistors 4903 and 4904 are coupled to the outputs of stage 4906of shift register 4905. Shift register 4905 enables each row to becontrolled independently, in accordance with the input data pattern(which is loaded in the beginning of an ANN operation)

FIG. 50 depicts word line driver 5000. Word line driver 5000 is similarto word line driver 4900, except that each select transistor is furthercoupled to a capacitor, such as capacitor 5001. Capacitor 5001 canprovide a pre-charge or bias to the word line at the beginning of anoperation, enabled by transistor 5002 to sample the voltage on line5003. Capacitor 5001 acts to sample and hold (S/H) the input voltage foreach wordline. Transistors 5004 and 5005 are off during the ANNoperation (array current summer and activation function) of the VMMarray, meaning that the voltage on the S/H capacitor 5001 will serve asa (floating) voltage source for the respective wordline. Alternatively,capacitor 5001 can be provided by the word line (or as a control gatecapacitance if the input is on a control gate) capacitance from the VMMarray.

It should be noted that, as used herein, the terms “over” and “on” bothinclusively include “directly on” (no intermediate materials, elementsor space disposed therebetween) and “indirectly on” (intermediatematerials, elements or space disposed therebetween). Likewise, the term“adjacent” includes “directly adjacent” (no intermediate materials,elements or space disposed therebetween) and “indirectly adjacent”(intermediate materials, elements or space disposed there between),“mounted to” includes “directly mounted to” (no intermediate materials,elements or space disposed there between) and “indirectly mounted to”(intermediate materials, elements or spaced disposed there between), and“electrically coupled” includes “directly electrically coupled to” (nointermediate materials or elements there between that electricallyconnect the elements together) and “indirectly electrically coupled to”(intermediate materials or elements there between that electricallyconnect the elements together). For example, forming an element “over asubstrate” can include forming the element directly on the substratewith no intermediate materials/elements therebetween, as well as formingthe element indirectly on the substrate with one or more intermediatematerials/elements there between.

1. A system comprising: a plurality of vector-by-matrix multiplicationarrays in an analog neural memory system, each vector-by-matrixmultiplication array comprising an array of non-volatile memory cellsorganized into rows and columns, wherein each memory cell comprises aword line terminal; a plurality of read row decoders, each read rowdecoder coupled to one of the plurality of vector-by-matrixmultiplication arrays for applying a voltage to one or more selectedrows during a read operation; and a shared program row decoder coupledto all of the plurality of vector-by-matrix multiplication arrays forapplying a voltage to one or more selected rows in one or more of thevector-by-matrix multiplication arrays during a program operation. 2.The system of claim 1, wherein the non-volatile memory cells aresplit-gate flash memory cells.
 3. The system of claim 1, wherein thenon-volatile memory cells are stacked-gate flash memory cells.
 4. Amethod comprising: applying, by a shared program row decoder coupled toa plurality of vector-by-matrix multiplication arrays of non-volatilememory cells, a voltage during a program operation to one or moreselected rows in the plurality of vector-by-matrix multiplicationarrays.
 5. The method of claim 4, comprising: applying, by a pluralityof read row decoders, wherein each read row decoder is coupled to one ofthe plurality of vector-by-matrix multiplication arrays in an analogneural memory system, a voltage to one or more selected rows ofnon-volatile memory cells during a read operation.
 6. The method ofclaim 4, wherein the non-volatile memory cells are split-gate flashmemory cells.
 7. The method of claim 4, wherein the non-volatile memorycells are stacked-gate flash memory cells.